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Computational Intelligence for Smart Sensor Networks: Possibilities and Prospects for Soft Computing-Based Sensors

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 18006

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Prof. Dr. Jason Levy

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Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
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Special Issue Information

Dear Colleagues,

The use of smart sensors is one of the most timely and important topics in computational intelligence and sensor networks. Sensor intelligence is essential in all layers of a hierarchical sensor network: Computation Intelligence (CI) tools and Soft Computing-based solutions have demonstrated the ability to produce robust, high-quality, and human-competitive results at each level of the sensor system architecutres In particual, fuzzy-neural deep learning and the concomitant development of evolutionary algorithms, learning theory, probabilistic methods and genetic programming have helped to transform sensor research and design. For example, there has been an explosion in the development of smart, wearable sensors to monitor human activities and the development of novel information fusion algorithms for sensors networks. We are particularly interested in papers that examine examine novel soutions for computational efficiency (power control) and swam intelligence in sensor networks. This involves a large array of cooperative processing sensor approaches including fog and edge computing. The application of Computational Intelligence to sensor systems continues to be one of the most in-demand areas of research and pratice as evidenced by the following applications of smart computing for sensor systems:

Cooperative processing (swarm intelligence, fog computing, etc.) in sensor networks
Green Cyber Physical Systems (CPS) and Internet of Things
Data analytics, cloud computing and wireless sensor networks
Wearable sensors and Privacy
Soft sensor systems
Biomedical applications;
Big Data analytics;
Extreme computing;
Intelligent manufacturing;
Autonomous systems and industrial processes optimization;
Hyper-parameter learning (learning and tuning of sensor parameters, automatic calibration)
Indirect measurements
Computer vision and image processing;
Parallel and distributed computing.
Information fusion in sensor networks
Wireless sensor networks;
Cloud and swarm robotics;
Management of complex, noisy datasets
data aggregation
Sensitivity and robustness analysis
Computational efficiency (power control)
Human measurements and Social sensing

The Special Issue will publish original research, reviews and applications in the field of Computational Intelligence techniques (fuzzy logic, artificial neural networks, evolutionary computing, learning theory and probabilistic methods) applied to sensor systems. The papers in this Special Issue should specifically relate to the interface between sensors and computational intelligence.

Prof. Dr. Jason K. Levy
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

17 pages, 564 KiB

Open AccessArticle

TCQG—Software-Defined Transmission Control Scheme in 5G Networks from Queuing Game Perspective

by Chao Guo, Cheng Gong, Juan Guo, Haitao Xu and Long Zhang

Sensors 2019, 19(19), 4170; https://doi.org/10.3390/s19194170 - 26 Sep 2019

Cited by 4 | Viewed by 2151

Abstract

The efficient processing and forwarding of big data is one of the key problems and challenges facing the next generation wireless communication network. Using a software definition method to virtualize the network can improve the efficiency of network operation and reduce the cost of network operation and maintenance. A software-defined transmission control scheme was presented to solve the excessive controller flow problem for 5G networks. Based on the queuing game theory, a system model was built due to the competition among the requests of the switch. The transmission control platform was in charge of resource allocation. It got maximum social welfare under a profit-maximizing fee. In this model, the optimal queue length was calculated and discussed in a first-come-first-served and last-come-first-served with preemption discipline. The optimal queue length was obtained and the optimal admission fee was calculated. Then, the single switch single controller transmission control model was extended to the multi-switches single controller model. As a result, the social welfare of the system containing the controller’s profit and switch surplus reaches the maximum. Full article

(This article belongs to the Special Issue Computational Intelligence for Smart Sensor Networks: Possibilities and Prospects for Soft Computing-Based Sensors)

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29 pages, 3539 KiB

Open AccessArticle

Resilient Multiuser Session Control in Softwarized Fog-Supported Internet of Moving Thing Systems

by Helber Wagner da Silva and Augusto José Venâncio Neto

Sensors 2019, 19(12), 2766; https://doi.org/10.3390/s19122766 - 20 Jun 2019

Cited by 1 | Viewed by 2528

Abstract

The combination of IoT and mobility promises to open a new frontier of innovations in smart environments, through the advent of the Internet of Moving Things (IoMT) paradigm. In IoMT, an array of IoT devices leverage IP-based mobile connectivity to provide a vast range of data ubiquitously. The IoMT realization will foster smart environments at unprecedented levels, by efficiently affording services and applications whereby today’s technologies make their efficiency unfeasible, such as autonomous driving and in-ambulance remotely-assisted patient. IoMT-supported mission-critical applications push computing and networking requirements to totally new levels that must be met, raising the need for refined approaches that advance beyond existing technologies. In light of this, this paper proposes the Resilient MultiUser Session Control (ReMUSiC) framework, which deploys emerging softwarization and cloudification technologies to afford flexible, optimized and self-organized control plane perspectives. ReMUSiC extends our previous work through the following innovations. A quality-oriented resilience mechanism is capable of responding to network dynamics events (failure and mobility) by readapting IoMT multiuser mobile sessions. A softwarized networking control plane that allows to, at runtime, both fetch current network state and set up resources in the attempt to always keep affected IoMT multiuser mobile sessions best-connected and best-served. A cloudification approach allows a robust environment, through which cloud- and fog-systems interwork to cater to performance-enhanced capabilities. The IoMT’s suitability and performance impacts by ReMUSiC framework use are assessed through real testbed prototyping. Impact analysis in Quality of Service (QoS) performance and perceived Quality of Experience (QoE), demonstrate the remarkable abilities of the ReMUSiC framework, over a related approach, in keeping IoMT multiuser mobile sessions always best-connected and best-served. Full article

(This article belongs to the Special Issue Computational Intelligence for Smart Sensor Networks: Possibilities and Prospects for Soft Computing-Based Sensors)

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17 pages, 2187 KiB

Open AccessArticle

Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System

by Haiyuan Wang and Hongxian Tian

Sensors 2019, 19(8), 1938; https://doi.org/10.3390/s19081938 - 25 Apr 2019

Cited by 1 | Viewed by 2854

Abstract

Low-Power Wide-Area Network (LPWAN) is the technology that the Internet-of-Things (IoT) uses in long-distance, wide-coverage scenarios. As one of the ultra-narrowband (UNB) modulation techniques, M-ary position phase shift keying (MPPSK) modulation can provide high coverage and high reliability for LPWAN. This paper proposes a multipath separation method based on MPPSK modulation, which aims to eliminate the influence of multipath on the main path without increasing the spectrum overhead and system complexity. Specifically, the modulation parameter of the system is adjusted according to the delay value, so that the phase jump of the multipath signal falls outside the phase jump of the main path symbol to achieve separation of the multipath from the main path. Moreover, a normalized symbol joint decision method is proposed in order to reduce the introduced noise while using multipath information for decisions. The simulation results indicate that the multipath separation conditions given in this paper can meet the requirements of multipath separation of MPPSK signals. Compared with the existing mainstream decision scheme, the normalized symbol joint decision improves the demodulation performance of the system. Full article

(This article belongs to the Special Issue Computational Intelligence for Smart Sensor Networks: Possibilities and Prospects for Soft Computing-Based Sensors)

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28 pages, 9230 KiB

Open AccessArticle

Dynamic Flying Ant Colony Optimization (DFACO) for Solving the Traveling Salesman Problem

by Fadl Dahan, Khalil El Hindi, Hassan Mathkour and Hussien AlSalman

Sensors 2019, 19(8), 1837; https://doi.org/10.3390/s19081837 - 17 Apr 2019

Cited by 53 | Viewed by 5427

Abstract

This paper presents an adaptation of the flying ant colony optimization (FACO) algorithm to solve the traveling salesman problem (TSP). This new modification is called dynamic flying ant colony optimization (DFACO). FACO was originally proposed to solve the quality of service (QoS)-aware web service selection problem. Many researchers have addressed the TSP, but most solutions could not avoid the stagnation problem. In FACO, a flying ant deposits a pheromone by injecting it from a distance; therefore, not only the nodes on the path but also the neighboring nodes receive the pheromone. The amount of pheromone a neighboring node receives is inversely proportional to the distance between it and the node on the path. In this work, we modified the FACO algorithm to make it suitable for TSP in several ways. For example, the number of neighboring nodes that received pheromones varied depending on the quality of the solution compared to the rest of the solutions. This helped to balance the exploration and exploitation strategies. We also embedded the 3-Opt algorithm to improve the solution by mitigating the effect of the stagnation problem. Moreover, the colony contained a combination of regular and flying ants. These modifications aim to help the DFACO algorithm obtain better solutions in less processing time and avoid getting stuck in local minima. This work compared DFACO with (1) ACO and five different methods using 24 TSP datasets and (2) parallel ACO (PACO)-3Opt using 22 TSP datasets. The empirical results showed that DFACO achieved the best results compared with ACO and the five different methods for most of the datasets (23 out of 24) in terms of the quality of the solutions. Further, it achieved better results compared with PACO-3Opt for most of the datasets (20 out of 21) in terms of solution quality and execution time. Full article

(This article belongs to the Special Issue Computational Intelligence for Smart Sensor Networks: Possibilities and Prospects for Soft Computing-Based Sensors)

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28 pages, 11742 KiB

Open AccessArticle

Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System

by Dan-feng Zhao, Hai Tian and Rui Xue

Sensors 2019, 19(5), 1067; https://doi.org/10.3390/s19051067 - 02 Mar 2019

Cited by 5 | Viewed by 4376

Abstract

This paper studies an adaptive coding scheme for B5G (beyond 5th generation) mobile system-enhanced transmission technology. Different from the existing works, the authors develop a class of rate-compatible, non-binary, low-density parity check (RC-NB-LDPC) codes, which expresses the strong connection between the algebra-based and graph-theoretic-based constructions. The constructed codes can not only express rate-compatible (RC) features, but also possess a quasi-cyclic (QC) structure that facilitates the encoding implementation. Further, in order to achieve the code rate-adaptive allocation scheme, the authors propose using the K-means++ clustering algorithm to cluster different channel environments, considering various factors that affect channel characteristics. Finally, in order to present the advantages of the adaptive coding scheme, the authors construct a coding scheme for image transmission. The numerical results demonstrate that the developed code can obtain better waterfall performance in a larger code rate range, which is more suitable for data transmission; the adaptive coding transmission scheme can obtain higher reconstructed image quality compared to the fixed code rate-coding scheme. Moreover, when considering unequal error protection (UEP), the proposed scheme can further improve the reconstructed image quality. Full article

(This article belongs to the Special Issue Computational Intelligence for Smart Sensor Networks: Possibilities and Prospects for Soft Computing-Based Sensors)

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